An exploration study on factors influencing Iranian food industry

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*Corresponding author.

E-mail addresses: dr.naserazad@yahoo.com (N. Azad)

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Management Science Letters

homepage: www.GrowingScience.com/msl

An exploration study on factors influencing Iranian food industry

Naser Azad*, Seyed Mohsen Seyedaliakbar, Arash Hosseinzadeh and Ashkan Arabi

Department of Management, Islamic Azad University, South Tehran Branch, Tehran, Iran

C H R O N I C L E A B S T R A C T

Article history:

Received January 12, 2013 Received in revised format 15 April 2013

Accepted 16 April 2013 Available online April 17 2013

The proposed study of this paper present an empirical investigation to detect important factors impacting on food market using factor analysis. The proposed study designed a questionnaire, distributed among 207 customers who were regular customers of two food chains in city of Tehran, Iran named Shahrvand and Hyperstar. The results of our survey indicate that six major factors including brand loyalty, physical characteristics, pricing effects, performance characteristics, brand relationship and brand position influence food industry, significantly. In terms of the first factor, brand loyalty, “Trust”, “Packaging design characteristics”, “Competitive pricing strategy”, “Stability in quality”, “External relationships” and “Meeting expectations” are important factors in different categories.

Factor analysis Food industry Critical factors

1. Introduction

The role of brands and branding in the new economy characterized by digitization and globalization are attracting considerable attention (Fernie, 1990; Dawar & Parker, 1994; Dowling & Uncles, 1997;

Rowley, 2004). Morgan-Thomas and Veloutsou (2013) presented some insights on marketing and

information systems research to build a framework of online brand experience. In their model, emotional characteristics of brand relationship supplemented the dimension of technology acceptance to reach at a comprehensive insight about consumer experience with an online brand. The empirical experiments involved structural equation modeling based on a survey of 456 users of online search engines. The results demonstrated that trust and perceived usefulness positively influenced online brand experience. Positive experiences result in satisfaction and behavioral intentions that in turn led to the formation of online brand relationship. In their survey, brand reputation emerged as an important antecedent of trust and perceived ease of implementation of an online brand (Sudhir, 2001).

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developing relationships between brands and consumers. Ha and Perks (2005) studied the effects of consumer perceptions of brand experience on the web by looking into brand familiarity, satisfaction and brand trust. They discussed that creating a customer experience that is synonymous with a particular website could be recognized as an essential driver of e-performance, increasingly.

E-tailors attempt to impact consumers' shopping behavior, through atmospherics and service, as brick-and-mortar stores. They investigated several unanswered questions in recent studies of consumer behavior in the context of internet-based marketing. The results of an empirical study of e-consumer behavior demonstrated that brand trust was achieved through the following dimensions such as various brand experiences and the search for information, a high level of brand familiarity, and customer satisfaction based on cognitive and emotional factors (Rettie & Brewer, 2000; Chattopadhyay & Laborie, 2005). Dickson and Urbany (1994) investigated retailer reactions to competitive price changes. Gabisch and Gwebu (2011) examined the effect of virtual experiences on attitude formation, and offline purchase intentions, and detected three kinds of channel congruence including perceived diagnosticity, self-image congruence, and behavioral consistency, which could help describe the cross-channel effects (Underwood, 2001). They reported that multichannel impacts existed between virtual brand experiences and real-world purchasing decisions. According to Alloza (2008), Successful corporate brand management lies on sounded brand engagement and strategic alignment initiatives. Kim and Sullivan (1998) investigated the impact of parent brand experience on line extension trial and repeat purchase. Iglesias et al. (2011) studied the direct and indirect relationship between brand experience and brand loyalty. They investigated whether the relationship was mediated by affective commitment or not. The analysis recommended that affective commitment could mediate the relationship between brand experience and brand loyalty for all three product studied categories including cars, laptops and sneakers. The article extended the understanding of the brand experience construct by studying its impact on brand loyalty and by incorporating affective commitment as a mediating variable.

Morrison and Crane (2007) discussed why marketers of service brands must understand the emotional dynamics involved when a customer chooses and decides to continue to implement a service brand. It also presents practical guidance for how marketers are capable of building strong service brands by creating and managing emotional brand experiences. Hultén (2011) presented the multi-sensory brand-experience concept in association with the human minds and senses and tried to propose a sensory marketing (SM) model of the multi-sensory brand-experience hypothesis (Keller, 2011). The findings offered additional insights to managers on the multi-sensory brand-experience concept. Boo et al. (2009) examined empirical information to develop a destination brand model by investigating customer-based brand equity models through a scale purification process, ensuring its reliability and validity. Zarantonello and Schmitt (2010) used the brand experience scale to profile consumers and predict consumer behavior. Clatworthy (2012) described the development and evaluation of a process model to transform brand strategy into service experiences during the front end of new service development. O'Cass and Grace (2004) explored different consumer experiences with a service brand. Coulson (2000) presented an application of the stages of change model to consumer use of food labels. Méndez et al. (2006) analyzed price dispersion tools available to consumer goods manufacturers to obtain price consistency.

2. The proposed study

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N. Azad et al. / Management Science Letters 3 (2013)

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management, stores created experience, physical design of store, familiarity, satisfaction, face to face relationships, employee’s behavior, perception image, social background, response to expectations, sustainability of brand, external advertisement, packaging design, quality of packaging, quality durability, access and price. Cronbach alpha was calculated as 0.797, which is well above the minimum acceptable limit and validates the results. Table 1 shows details of some basic statistics,

Table 1

Descriptive Statistics

N Range Skewness Kurtosis

Statistic Statistic Statistic Std. Error Statistic Std. Error

VAR00001 206 4.00 -.654 .169 1.323 .337

VAR00002 206 3.00 -.156 .169 -.408 .337

VAR00003 206 4.00 -.339 .169 -.427 .337

VAR00004 206 4.00 -.662 .169 .333 .337

VAR00005 206 4.00 -.378 .169 -.222 .337

VAR00006 206 4.00 -.256 .169 -.513 .337

VAR00007 206 4.00 -.663 .169 .370 .337

VAR00009 206 4.00 -.654 .169 .344 .337

VAR00010 206 4.00 -.707 .169 -.040 .337

VAR00011 206 4.00 -.620 .169 .876 .337

VAR00012 206 4.00 -.092 .169 -.300 .337

VAR00013 206 4.00 .026 .169 -.165 .337

VAR00014 206 4.00 -.582 .169 .284 .337

VAR00015 206 3.00 -.196 .169 -.422 .337

VAR00016 206 4.00 -.114 .169 -.055 .337

VAR00017 206 4.00 -.366 .169 .404 .337

VAR00018 206 4.00 -.341 .169 -.265 .337

VAR00019 206 3.00 -.810 .169 -.096 .337

VAR00020 206 4.00 -.662 .169 .513 .337

VAR00021 206 4.00 -.397 .169 -.628 .337

VAR00022 206 4.00 -.898 .169 .194 .337

Normal Score of VAR00008 using Blom's

Formula 206 3.1447 -.832 .169 -.163 .337

Normal Score of VAR00023 using Blom's

Formula 206 3.0526 -.999 .169 -.037 .337

Valid N (listwise) 206

Table 2

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation(1)

Squared Multiple Correlation(2)

Cronbach's Alpha if Item Deleted(3)

VAR00001 75.676793 67.212 .379 .443 .789

VAR00002 75.953492 67.476 .326 .301 .791

VAR00003 76.074851 64.512 .461 .369 .783

VAR00004 75.880677 66.125 .345 .231 .790

VAR00005 76.069997 68.511 .178 .140 .799

VAR00006 76.235045 65.056 .367 .260 .788

VAR00007 75.836987 64.721 .496 .361 .782

VAR00009 76.050579 67.060 .261 .147 .795

VAR00010 75.701065 66.233 .357 .203 .789

VAR00011 75.977764 67.299 .314 .218 .791

VAR00012 76.696211 66.304 .286 .223 .793

VAR00013 76.594269 68.225 .195 .164 .798

VAR00014 75.841842 66.021 .403 .266 .787

VAR00015 75.972910 64.464 .561 .422 .779

VAR00016 76.536016 68.013 .208 .227 .797

VAR00017 76.167084 66.059 .387 .458 .787

VAR00018 76.065143 64.849 .459 .444 .784

VAR00019 75.560288 66.022 .412 .382 .786

VAR00020 75.953492 66.173 .353 .300 .789

VAR00021 75.934075 67.801 .215 .371 .797

VAR00022 75.662230 66.568 .314 .390 .791

Normal Score of VAR00008 using

Blom's Formula 79.874675 66.910 .351 .370 .789

Normal Score of VAR00023 using

Blom's Formula 79.884643 67.394 .320 .305 .791

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Fig. 1. The results of Scree plot

The result of Fig. 1 demonstrates that after six factors the trend becomes linear. Table 3 presents details of factor analysis before rotation implemented.

Table 3

The results of Principal Component Analysis before rotation

Component

1 2 3 4 5 6 7 8

VAR00015 .673

VAR00007 .627

VAR00018 .577

VAR00003 .574

VAR00019 .527 -.361

VAR00001 .524 -.382

VAR00014 .515 -.343

Normal Score of VAR00008 using Blom's Formula .481 -.436

VAR00006 .454 .381

VAR00010 .444

VAR00002 .433 -.383 .366

VAR00004 .432 .416

VAR00021 .694 .373

VAR00022 .350 .675

VAR00020 .406 .474

VAR00017 .495 .576

VAR00016 .511 .427 -.345

Normal Score of VAR00023 using Blom's Formula .390 .401 -.456

VAR00012 .660

VAR00005 .589 .492

VAR00011 .402 -.424 -.336

VAR00013 .436 -.494 .455

VAR00009 .347 -.474

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Table 4

The results of Principal Component Analysis using Varimax with Kaiser Normalization Component

1 2 3 4 5 6 7 8

VAR00001 .832

Normal Score of VAR00008 using Blom's Formula .642

VAR00003 .568 .408

VAR00002 .550 .430

VAR00014 .504

VAR00007 .441 .434

VAR00017 .812

VAR00018 .728

VAR00015 .339.577

VAR00006 .495

VAR00010

VAR00021 .838

VAR00022 .774

VAR00020 .540 .363

VAR00019 .774

Normal Score of VAR00023 using Blom's Formula .373 .576

VAR00011 .718

VAR00012 .471 .389

VAR00016 .739

VAR00009 .523

VAR00005 .825

VAR00013 .888

VAR00004 .336 .363 .430

3. The results

The proposed study of this paper has determined six major factors using factor analysis and in this section, we present details of our findings.

3.1. The first factor: Brand loyalty

The first factor, “Brand loyalty” includes four components including “trust”, “Customer satisfaction”, “Perception profitability” and “Brand awareness” and the results are summarized in Table 5.

Table 5

The summary of factors associated with brand loyalty Option

Factor Eigenvalueweight % ofvariance Accumulated

Trust

.832 4.583 19.927 19.927

Customer satisfaction .642

Perception profitability .550

Brand awareness

.504

Cronbach alph =0.789

It is evident from the results of Table 5 that “Trust” is number one priority followed by “Customer satisfaction”, “Perception profitability” and “Brand awareness”. Cronbach alpha has been calculated as 0.789, which validates the results of our survey.

3.2. The second factor: Physical characteristics

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The summary of factors associated with compatibility Option

Factor Eigenvalueweight %ofvariance Accumulated

Packaging design characteristics

.812 0.533 2.404 89.247

Quality of packaging

.728

Brand revokes .577

Physical design of stores

.495

According to the results of Table 6, “Packaging design characteristics” is the most important factor followed by “Quality of packaging”, “Brand revokes” while “Physical design of stores” is the last priority.

3.3. The third factor: Pricing effects

Pricing effects is the third important factor influencing food industry, which includes three factors including “Competitive price”, “Price stability”, and “Product availability”. Table 7 demonstrates details of our survey where “Competitive pricing strategy” plays essential role on marketing food industry followed by “Price stability”.

Table 7

The summary of factors associated with pricing effects Option

Competitive pricing strategy

.838 0.387 1.685 97.186

Price stability

.774

Product availability .540

3.4. The fourth factor: Performance characteristics

Performance characteristics components is the next factor, which influences food marketing and it includes three factors summarized in Table 8 as follows,

Table 8

The summary of factors associated with performance characteristics Option

Brand awareness

.434

Stability in quality

.774 0.480 2.089 93.557

Minimum price, maximum productivity .576

Based on the results of Table 8, “Stability in quality” is the most important factor followed by “minimum price, maximum productivity” and “brand awareness”.

3.5. The fifth factor: Brand relationship

Brand relationship is the next factor, which influences food marketing including three factors summarized in Table 9 as follows,

Table 9

The summary of factors associated with brand relationship Option

Brand reputation

.408

External relationships

.739 0.562 2.443 86.843

Face to face relationship .523

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Based on the results of Table 9, “External relationships” is the most important factor followed by “face to face relationship” and “brand reputation”.

3.6. The sixth factor: Brand position

Brand position is the last factor, which influences food marketing and it includes three factors summarized in Table 10 as follows,

Table 10

The summary of factors associated with brand position Option

Social position

.389

Meeting expectations

.888 0.689 2.997 78.916

Customer relationship management .430

Based on the results of Table 10, “Meeting expectations” is the most important factor followed by “Customer relationship management” and “Social position”.

4. Discussion and conclusion

In this paper, we have presented an empirical investigation using principal component analysis to detect important factors influencing brand position. The results of our survey have revealed six major factors including brand loyalty, physical characteristics, pricing effects, performance characteristics, brand relationship and brand position. In terms of the first factor, brand loyalty, “Trust” is number one priority followed by “Customer satisfaction”, “Perception profitability” and “Brand awareness”. In terms of the second factor, physical characteristics, “Packaging design characteristics” is the most important factor followed by “Quality of packaging”, “Brand revokes” while “Physical design of stores” is the last priority. In terms of pricing effects, our survey indicate that “Competitive pricing strategy” plays essential role on marketing food industry followed by “Price stability”. In terms of performance characteristics, “Stability in quality” is the most important factor followed by “minimum price, maximum productivity” and “brand awareness”. Brand relationship is another influencing factor on food industry where “External relationships” in this category is the most important factor followed by “face to face relationship” and “brand reputation”. Finally, brand position, is the last factor in our analysis where “Meeting expectations” is the most important factor followed by “Customer relationship management” and “Social position”.

Acknowledgment

The authors would like to thank anonymous referees for constructive comments on earlier version of this paper.

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